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Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

Author

Listed:
  • Po Yun

    (School of Economics and Management, Hefei University, Hefei 230601, China)

  • Chen Zhang

    (School of Management, Hefei University of Technology, Hefei 230601, China)

  • Yaqi Wu

    (School of Economics, North Minzu University, Yinchuan 750021, China)

  • Yu Yang

    (School of Economics and Management, Anhui Jianzhu University, Hefei 230601, China)

Abstract

The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

Suggested Citation

  • Po Yun & Chen Zhang & Yaqi Wu & Yu Yang, 2022. "Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network," IJERPH, MDPI, vol. 19(2), pages 1-19, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:2:p:899-:d:724550
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    References listed on IDEAS

    as
    1. Chevallier, Julien, 2011. "Detecting instability in the volatility of carbon prices," Energy Economics, Elsevier, vol. 33(1), pages 99-110, January.
    2. Benz, Eva & Trück, Stefan, 2009. "Modeling the price dynamics of CO2 emission allowances," Energy Economics, Elsevier, vol. 31(1), pages 4-15, January.
    3. Han, Meng & Ding, Lili & Zhao, Xin & Kang, Wanglin, 2019. "Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors," Energy, Elsevier, vol. 171(C), pages 69-76.
    4. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    5. Sun, Wei & Huang, Chenchen, 2020. "A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network," Energy, Elsevier, vol. 207(C).
    6. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Le, TN-Lan & Leyva-de la Hiz, Dante I., 2021. "Markov-switching dependence between artificial intelligence and carbon price: The role of policy uncertainty in the era of the 4th industrial revolution and the effect of COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    7. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    8. Gary Koop & Lise Tole, 2013. "Forecasting the European carbon market," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 723-741, June.
    9. Chevallier, Julien, 2009. "Carbon futures and macroeconomic risk factors: A view from the EU ETS," Energy Economics, Elsevier, vol. 31(4), pages 614-625, July.
    10. Aktar, Asikha & Alam, Md. Mahmudul & Al-Amin, Abul Quasem, 2021. "Global Economic Crisis, Energy Use, CO2 Emissions and Policy Roadmap amid COVID-19," OSF Preprints 69kje, Center for Open Science.
    11. Wen, Fenghua & Zhao, Haocen & Zhao, Lili & Yin, Hua, 2022. "What drive carbon price dynamics in China?," International Review of Financial Analysis, Elsevier, vol. 79(C).
    12. Jianguo Zhou & Shiguo Wang, 2021. "A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors," Energies, MDPI, vol. 14(5), pages 1-20, March.
    13. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    14. Seifert, Jan & Uhrig-Homburg, Marliese & Wagner, Michael, 2008. "Dynamic behavior of CO2 spot prices," Journal of Environmental Economics and Management, Elsevier, vol. 56(2), pages 180-194, September.
    15. Kim, Hyun Seok & Koo, Won W., 2010. "Factors affecting the carbon allowance market in the US," Energy Policy, Elsevier, vol. 38(4), pages 1879-1884, April.
    16. repec:dau:papers:123456789/4210 is not listed on IDEAS
    17. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    18. Oberndorfer, Ulrich, 2009. "EU Emission Allowances and the stock market: Evidence from the electricity industry," Ecological Economics, Elsevier, vol. 68(4), pages 1116-1126, February.
    19. Po Yun & Chen Zhang & Yaqi Wu & Xianzi Yang & Zulfiqar Ali Wagan, 2020. "A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    20. Daskalakis, George & Psychoyios, Dimitris & Markellos, Raphael N., 2009. "Modeling CO2 emission allowance prices and derivatives: Evidence from the European trading scheme," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1230-1241, July.
    21. Zhang, Chen & Yun, Po & Wagan, Zulfiqar Ali, 2019. "Study on the wandering weekday effect of the international carbon market based on trend moderation effect," Finance Research Letters, Elsevier, vol. 28(C), pages 319-327.
    22. repec:dau:papers:123456789/5110 is not listed on IDEAS
    23. Zhu, Bangzhu & Ye, Shunxin & Wang, Ping & He, Kaijian & Zhang, Tao & Wei, Yi-Ming, 2018. "A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting," Energy Economics, Elsevier, vol. 70(C), pages 143-157.
    24. Ajay K. Dhamija & Surendra S. Yadav & PK Jain, 2017. "Forecasting volatility of carbon under EU ETS: a multi-phase study," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(2), pages 299-335, April.
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    1. Peng Ye & Yong Li & Abu Bakkar Siddik, 2023. "Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm," Energies, MDPI, vol. 16(11), pages 1-39, June.

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